Name
Title | ||
|---|---|---|
Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification |
Abstract | ||
|---|---|---|
Cellular Electron Cryo Tomography (CECT) 3D imaging has permitted biomedical community to study macromolecule structures inside single cells with deep learning approaches. Many deep learning-based methods have since been developed to classify macromolecule structures from tomograms with high accuracy. However, several recent studies have demonstrated the lack of robustness in these models against often-imperceptible, designed changes of input. Therefore, making existing subtomogram-classification models robust remains a serious challenge. In this paper, we study the robustness of the state-of-the-art subtomogram classifier on CECT images and propose a method called Regularized Adversarial Training (RAT) to defend the classifier against a wide range of designed threats. Our results show that RAT improves robustness for CECT image classification over the previous methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8982954 | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
Cellular Electron Cryo Tomography,Classification,Robustness,Adversarial Training,Adversarial Attacks | Computer science,Tomography,Robustness (computer science),Artificial intelligence,Deep learning,Classifier (linguistics),Contextual image classification,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-7281-1868-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xindi Wu,Yijun Mao,Haohan Wang,Xiangrui Zeng,Xin Gao,Eric P. Xing,Min Xu | 1 | 0 | 0.34 |
| Yijun Mao | 2 | 0 | 0.34 |
| Haohan Wang | 3 | 32 | 7.26 |
| Xiangrui Zeng | 4 | 9 | 11.35 |
| Xin Gao | 5 | 598 | 64.98 |
| Bo Xing | 6 | 7332 | 471.43 |
| Min Xu | 7 | 5 | 3.19 |